class_docstr: implementation of HighRes3DNet:
    Li et al., "On the compactness, efficiency, and representation of 3D
    convolutional networks: Brain parcellation as a pretext task", IPMI '17
class_docstr_tok: ["implementation","of","HighRes3DNet",":","Li","et","al.",",","``","On","the","compactness",",","efficiency",",","and","representation","of","3D","convolutional","networks",":","Brain","parcellation","as","a","pretext","task","''",",","IPMI","'17"]
code: def __init__(
      self,
      num_classes,
      w_initializer=None,
      w_regularizer=None,
      b_initializer=None,
      b_regularizer=None,
      acti_func="prelu",
      name="FinalClassSeg",
  ):
      super(ClassSegFinnet, self).__init__(
          num_classes=num_classes,
          w_initializer=w_initializer,
          w_regularizer=w_regularizer,
          b_initializer=b_initializer,
          b_regularizer=b_regularizer,
          acti_func=acti_func,
          name=name,
      )
      self.num_classes = num_classes
      self.layers = [
          {"name": "fc_seg", "n_features": 10, "kernel_size": 1},
          {
              "name": "pool",
              "n_features": 10,
              "stride": 1,
              "func": "AVG",
          },
          {
              "name": "fc_seg",
              "n_features": num_classes,
              "kernel_size": 1,
          },
          {"name": "fc_class", "n_features": 2, "kernel_size": 1},
      ]
  
file_path: NiftyNet/niftynet/contrib/csv_reader/class_seg_finnet.py
imports: from __future__ import absolute_import, print_function
  from six.moves import range
  from niftynet.layer import layer_util
  from niftynet.layer.activation import ActiLayer
  from niftynet.layer.base_layer import TrainableLayer
  from niftynet.layer.bn import BNLayer
  from niftynet.layer.convolution import ConvLayer, ConvolutionalLayer
  from niftynet.layer.dilatedcontext import DilatedTensor
  from niftynet.layer.elementwise import ElementwiseLayer
  from niftynet.network.base_net import BaseNet
  from niftynet.layer.layer_util import infer_spatial_rank
  from niftynet.layer.fully_connected import FullyConnectedLayer
  import tensorflow
label_desc: Use image node_id 1 to create a new ClassSegFinnet object from inherited base classes: BaseNet with example: obj = ClassSegFinnet(num_classes, w_initializer, w_regularizer, b_initializer, b_regularizer, acti_func, name)